Mon premier blog

Approximation Algorithms for NP-Hard Problems by Dorit Hochbaum

Approximation Algorithms for NP-Hard Problems Dorit Hochbaum ebook
Page: 620
ISBN: 0534949681, 9780534949686
Format: djvu
Publisher: Course Technology

€� traveling salesperson problem, Steiner tree. Since many interesting optimization problems are computationally intractable (NP-Hard), we resort to designing approximation algorithms which provably output good solutions. Today is for its application to the field of hardness of approximation algorithms: It turns out that the PCP theorem is equivalent to saying that there are problems where computing even an approximate solution is NP-hard. I also wanted to include just a little bit of my own opinion on why studying approximation algorithms is worthwhile. A less obvious application is that the minimum spanning tree can be used to approximately solve the traveling salesman problem. In the Traveling Salesman is an NP-Hard problem. Many Problems are NP-Complete Does P=NP Coping with NP-Completeness The Vertex Cover Problem Smarter Brute-Force Search. Approximation algorithms for NP-hard problems. Currently we have approximation algorithms that can come up with “good solutions” in a fairly acceptable amount of time. Heuristics for NP-hard problems. Approaches include approximation algorithms, heuristics, average-case analysis, and exact exponential-time algorithms: all are essential. For graph estimation, we consider the problem of estimating forests with restricted tree sizes. The reason the Cooper result holds is essentially that Bayes nets can be used to encode boolean satisfiability (SAT) problems, so solving the generic Bayes net inference problem lets you solve any SAT problem.